Changes in version 2.2.0 (2025-01-15)                  

New features

  - New version of the vignette.

  - seqaddNA()now has apcont` argument, which allows to modify the
    probability that a gap with missing data will be continued.

Minor improvements and fixes

  - Bug regarding the frame.radius parameter that was always set as
    frame.radius=0, whatever the user provided corrected.

  - Bug regarding edge case with only one level in the dependent
    variable corrected.

  - For parallel computing, the maximum number of cores is set as
    availableCores()-1, instead as detectCores()-1.

  - Warnings now appear when the number of specified cores exceeds the
    number of multiple imputations, or the number of availableCores()-1.

  - Explanations of seqaddNA() have been deepened.

  - Explanations of seqimpute() regarding the slots of the seqimp object
    that is returned have been deepened.

  - The colors for the seqmissIplot() and seqmissfplot plots have been
    changed to improve readability.

  - Each variable of the imputed object now have the same levels.

  - The quality and speed of the code were increased.

  - Reference to new article about MICT-timing added.

                 Changes in version 2.1.0 (2024-11-13)                  

New features

  - seqimpute() now has an argument end.impute argument, which specifies
    if missing data at the end of sequences should be imputed or not.

  - seqmissIplot(), seqmissfplot() and seqmissimplic now have an
    argument void.miss, which specifies whether the void values of a
    provided object of class stslist should be considered as missings or
    not.

  - seqaddNA() now has an argument maxprop, which specifies the maximum
    proportion of missing data that is allowed to be simulated in a
    sequence.

Minor improvements and fixes

  - Fixes issues with seqimpute() when more than one row only with NA's
    end the dataset to impute.

  - In seqmissIplot() and seqmissfplot(), the states in the plots are
    now labeled as 'missing' and 'not missing' instead of 'missing' and
    'observed' to account for uneven sequence length.

  - Fixes issues in seqimpute() related to the preparation of the data
    when an object of class stslist, built with the TraMineR package is
    provided.

                 Changes in version 2.0.0 (2024-03-27)                  

Breaking changes

  - seqimpute() now returns an object of class seqimp. In particular,
    the include and mice.return arguments are no longer relevant and
    have been removed.

  - The OD argument has been renamed to data. The argument OD itself is
    deprecated.

  - The CO argument has been renamed to covariates. The argument CO
    itself is deprecated.

  - The COt argument has been renamed to time.covariates. The argument
    COt itself is deprecated.

  - The mi argument has been renamed to m. The argument mi itself is
    deprecated.

  - The dataset provided in the package used to be divided into three
    parts: the trajectories (OD), the covariates (CO), and the
    time-varying covariates (COt). They now appear as a single dataset,
    called gameadd.

  - seqimpute() no longer implements linear and ordinal regressions.

  - The default argument of m has been set to 5.

New features

  - seqimpute() implements the MICT-timing imputation algorithm. The
    argument timing indicates whether to use this algorithm or the MICT
    algorithm, and frame.radius specifies the radius of the time frame.

  - The user can now pass a dataset to the seqimpute() function and
    specify which columns correspond to the trajectories with the var
    argument, to the covariates with the covariates argument, and the
    time-varying covariates with the time.covariates argument.

  - A vignette has been added.

  - New seqmissfplot() plot function, which plots the most frequent
    patterns of missing data.

  - New seqmissIplot() plot function, which plots all patterns of
    missing data.

  - New seqmissimplic() function for identifying and visualizing the
    states that best characterize sequences with missing data.

  - New fromseqimp() function, which converts a seqimp object into a
    specified format.

  - New addcluster() function, which adds a clustering result to a
    seqimp object

  - New seqaddNA() function to simulate missing data.

  - New seqcomplete() function, which extracts all trajectories without
    missing data.

  - New seqwithmiss() function, which extracts all the trajectories with
    at least one missing value.

  - seqimpute() now returns an object of class seqimp. A print, summary,
    and plot functions have been added for this object type.

  - seqTrans() and seqQuickLook() now accept objects of class stslist
    built with the TraMineR package.

  - A ... argument has been added to seqimpute() to pass named arguments
    to the imputation functions.

Bug fixes

  - Fixes issues in seqimpute() related to the multinomial model when
    there is only one state in the dependent variable.

  - Fixes issues in seqimpute() related to random forest when one state
    does not appear in the dependent variable.

  - Fixes bug in seqimpute() when a single covariate is specified.

  - Fixes bug in seqimpute() related to long internal gaps.

  - Fixes bug in seqQuickLook() induced by datasets without missing
    data.